{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfdata = data.data\n",
    "\n",
    "dftargetnames = data.target_names\n",
    "\n",
    "dftarget = data.target\n",
    "\n",
    "dffeaturenames = data.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dfdata.shape\n",
    "dftargetnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=dfdata , columns=dffeaturenames)\n",
    "\n",
    "df['target'] = dftarget.reshape(150,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "print(data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)      target  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.199333    1.000000  \n",
       "std            0.762238    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ed41c83940>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ed423de1f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df.drop('target',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1 = pd.get_dummies(df['target'] , drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[['1' , '2']] = df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.drop('target' , axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['1' , '2'] , axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = dftarget.reshape(150,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns\n",
    "\n",
    "X = df[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
    "       'petal width (cm)']]\n",
    "\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(X_test , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0, 13,  0],\n",
       "       [ 0,  0, 13]], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "testcase = X.iloc[:5, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "correctanss = np.zeros((1,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.predict(testcase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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